Systems | Development | Analytics | API | Testing

Platform Engineering: Why Great Architecture Doesn't Win Developers

Akshi, Senior Engineering Manager at Pandora, explains why platform engineering succeeds only when reliable API infrastructure is paired with developer experience, trust, and intelligent governance. For platform leaders, API architects, and engineering teams building API and AI platforms, this talk turns adoption into a measurable product problem: reduce friction, create paved roads, and design guardrails that help both humans and AI agents move safely.

Kong in 5: AI Gateway 2.0 on Kubernetes

Kong AI Gateway 2.0 can be installed on Kubernetes in about five minutes using Kong Operator, Helm, and a self-managed control plane in Konnect. This walkthrough starts with a two-node cluster, creates a new AI Gateway in the Konnect UI, and installs Kong Operator with Helm. It then generates a personal access token, applies the API auth secret, and deploys the data plane through custom resources. You’ll see how to configure replicas, image, and load balancer ports in the YAML. This is the first video in the Kong in 5 series for Kong customers and users.

Playwright End-to-End Testing with Recorded API Mocks

Your Playwright test clicks Next page, waits for the item list, and times out. Now you’re digging through a trace to find out whether the page broke or an API call failed. Traditional browser-testing workflows focus on clicks, page state and UI assertions. Those checks tell you whether a user journey works, but investigating a failure also means understanding the APIs behind the page.

ReadyAPI vs. Postman for enterprise API testing

As API programs grow, teams need to test beyond individual requests. Functional checks, performance under load, security validation, and dependent services can involve different workflows, while teams still need a clear view of results and where tests run. Postman combines collaborative API development with collection-based functional and performance testing. SmartBear ReadyAPI brings functional, security, and performance testing together with service virtualization and broad protocol support.

Control the spend and usage of your AI testing agents | SmartBear BearQ

Worried an AI teammate could rack up a surprise bill? In this video, you’ll learn how SmartBear BearQ gives you real governance over AI testing spent – built with pre-baked monthly limits, so your team always knows what to expect before a single agent gets to work.

AI FinOps: Moving Beyond Cost Visibility to Control, Optimization, and Value

AI FinOps must not be an afterthought but should be a critical and mandatory component for any enterprise AI program.. The explosive growth of AI workloads from simple AI-assisted coding to product autonomous multiagent systems complicated by highly dynamic usage-based pricing has introduced next level risk in tracking and managing spend. As the Gartner report points out, 85% of enterprises have no automated controls to stop cost spikes.

Kong Insomnia 13.4: See Like Never Before

Kong Insomnia 13.4 is all about giving you more visibility than ever before. Whether it's your git changes, your secrets, or how your users are using Insomnia, you’re able to see more than ever before and make better decisions than ever. We’ve gotten great feedback on the native git support we shipped in v13 a few months ago, and many teams have switched to a CLI way of working. But other teams love the Insomnia Git UI.

Automated functional testing: How it works and tools

Automated functional testing means using scripts or tools to check that each feature of an application does what the requirements say, without a person clicking through every step. The tool sends the inputs, compares the result with what was expected and flags anything that breaks, on every build if you want it to. That is the textbook version. The practical version is simpler: you stop re-testing the login page by hand every Friday afternoon.